Learning to predict life and death from Go game records

Authors:
Erik C. D. van der Werf;Mark H. M. Winands;H. Jaap van den Herik;Jos W. H. M. Uiterwijk
Affiliations:
Department of Computer Science, Institute for Knowledge and Agent Technology, Universiteit Maastricht, P.O. Box 616, 6200 MD Maastricht, The Netherlands;Department of Computer Science, Institute for Knowledge and Agent Technology, Universiteit Maastricht, P.O. Box 616, 6200 MD Maastricht, The Netherlands;Department of Computer Science, Institute for Knowledge and Agent Technology, Universiteit Maastricht, P.O. Box 616, 6200 MD Maastricht, The Netherlands;Department of Computer Science, Institute for Knowledge and Agent Technology, Universiteit Maastricht, P.O. Box 616, 6200 MD Maastricht, The Netherlands
Venue:
Information Sciences: an International Journal
Year:
2005

Citing 5
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Abstract

This article presents a new learning system for predicting life and death in the game of Go. It is called Gone. The system uses a multi-layer perceptron classifier which is trained on learning examples extracted from game records. Blocks of stones are represented by a large amount of features which enable a rather precise prediction of life and death. On average, Gone correctly predicts life and death for 88% of all the blocks that are relevant for scoring. Towards the end of a game the performance increases up to 99%. A straightforward extension for full-board evaluation is discussed. Experiments indicate that the predictor is an important component for building a strong full-board evaluation function.